Epilepsy and headaches are two neurological disorders for which estimates of prevalence and associated factors remain poorly described in low and middle income countries (LMIC). Indeed, neuroepidemiologic studies from LMIC are subject to key epidemiologic biases that may lead to under- or overestimates of the prevalence and of magnitude of association of putative risk factors. These biases arise from failure to account for: 1) misclassification error of the disease due to imperfect diagnostic tests; 2) the spatial correlation of cases and 3) survey sampling methods. Current research has not comprehensively examined the impact of these potential biases in neuroepidemiology. This proposed project will address this gap by applying epidemiologic and spatial analytic methods to identify and address these epidemiologic biases for population estimates on two neurological disorders: epilepsy and severe chronic headaches among individuals in sub-Saharan Africa (SSA). Parasitic infections, particularly neurocysticercosis (NCC), and obstetric events in the context of low access to maternal health care have been identified as leading factors associated with epilepsy by the limited research in SSA. NCC often leads to several neurological disorders with the most common being seizures, epilepsy and progressively worsening severe chronic headaches. Additional contributing risk factors likely exist but have not been measured and may bias the magnitude of effect of the associated factors. The proposed study will address the following aims: Aim 1: To quantify the bias introduced by misclassification error when estimating the prevalence of epilepsy and severe chronic headaches in Burkina Faso; Aim 2: To examine the individual- and geographic level factors associated with the prevalence of epilepsy and severe chronic headaches in Burkina Faso while accounting for misclassification error by fitting a Bayesian hierarchical spatial model that allows for spatial correlation of the data; Aim 3: To investigate whether incorporating the sampling mechanism and post-stratification weights from survey data affect prevalence estimates of epilepsy across parishes in Uganda. The application of these methods will improve the validity of estimates for epilepsy and severe chronic headaches and quantify the impact of the biases in neuroepidemiologic studies conducted in LMIC. The findings from this research will advance the understanding of epilepsy and headaches and contribute to the limited research on neurological conditions conducted in SSA.